model.eval() eval_loss = 0 correct = 0 with torch.no_grad(): for batch in data_loader: data = batch['data'].to(device) labels = batch['label'].to(device) outputs = model(data) loss = criterion(outputs, labels) eval_loss += loss.item() _, predicted = torch.max(outputs, dim=1) correct += (predicted == labels).sum().item()
# Train the model for epoch in range(epochs): model.train() total_loss = 0 for batch in data_loader: data = batch['data'].to(device) labels = batch['label'].to(device) optimizer.zero_grad() outputs = model(data) loss = criterion(outputs, labels) loss.backward() optimizer.step() total_loss += loss.item() print(f'Epoch {epoch+1}, Loss: {total_loss / len(data_loader)}')
# Set hyperparameters num_classes = 8 input_dim = 128 batch_size = 32 epochs = 10 lr = 1e-4 training slayer v740 by bokundev high quality
# Define the Slayer V7.4.0 model class SlayerV7_4_0(nn.Module): def __init__(self, num_classes, input_dim): super(SlayerV7_4_0, self).__init__() self.encoder = nn.Sequential( nn.Conv1d(input_dim, 128, kernel_size=3), nn.ReLU(), nn.MaxPool1d(2), nn.Flatten() ) self.decoder = nn.Sequential( nn.Linear(128, num_classes), nn.Softmax(dim=1) )
# Load dataset and create data loader dataset = MyDataset(data, labels) data_loader = DataLoader(dataset, batch_size=batch_size, shuffle=True) labels) eval_loss += loss.item() _
import torch import torch.nn as nn import torch.optim as optim from torch.utils.data import Dataset, DataLoader
def __len__(self): return len(self.data) predicted = torch.max(outputs
Slayer V7.4.0 Developer: Bokundev Task: Training a high-quality model